ipsci

# Packages used in the ingestion of data
library(httr)     # Wrapper for the curl package.
library(XML)      # Dealing with text in xml.
library(jsonlite) # Getting and formatting JSON. 
# todo: install if not found
library(rplos)    # API to PLOS for journals.
library(readr)    # Reading rectangular data.
library(plyr)     # Wrangling data.
library(dplyr)    # Wrangling data.

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tibble)   # Tidier table object.
library(tidyr)    # Tidying data,
library(stringr)  # Manipulating strings. Using RegEx.
library(purrr)

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:plyr':
## 
##     compact

## The following object is masked from 'package:jsonlite':
## 
##     flatten

library(tm)       # Text mining.

## Loading required package: NLP

## 
## Attaching package: 'NLP'

## The following object is masked from 'package:httr':
## 
##     content

library(rjson)    # Work with JSON format.

## 
## Attaching package: 'rjson'

## The following objects are masked from 'package:jsonlite':
## 
##     fromJSON, toJSON

library(fulltext) # Text mining of open access journals.

## Registered S3 method overwritten by 'hoardr':
##   method           from
##   print.cache_info httr

## 
## Attaching package: 'fulltext'

## The following object is masked from 'package:dplyr':
## 
##     collect

# Special use case libraries
library(stats)
library(topicmodels) # for LDA topic modelling 
library(SnowballC) # for stemming

library(tidyverse) # general utility & workflow functions 

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.1     ✔ forcats 0.4.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::annotate() masks NLP::annotate()
## ✖ dplyr::arrange()    masks plyr::arrange()
## ✖ fulltext::collect() masks dplyr::collect()
## ✖ purrr::compact()    masks plyr::compact()
## ✖ NLP::content()      masks httr::content()
## ✖ dplyr::count()      masks plyr::count()
## ✖ dplyr::failwith()   masks plyr::failwith()
## ✖ dplyr::filter()     masks stats::filter()
## ✖ purrr::flatten()    masks jsonlite::flatten()
## ✖ rjson::fromJSON()   masks jsonlite::fromJSON()
## ✖ dplyr::id()         masks plyr::id()
## ✖ dplyr::lag()        masks stats::lag()
## ✖ dplyr::mutate()     masks plyr::mutate()
## ✖ dplyr::rename()     masks plyr::rename()
## ✖ dplyr::summarise()  masks plyr::summarise()
## ✖ dplyr::summarize()  masks plyr::summarize()
## ✖ rjson::toJSON()     masks jsonlite::toJSON()

library(tidytext) # text analysis in R 
library(lubridate) # working with times and dates   

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:plyr':
## 
##     here

## The following object is masked from 'package:base':
## 
##     date

library(rvest)    # Download and manipulate HTML.

## Loading required package: xml2

## 
## Attaching package: 'rvest'

## The following object is masked from 'package:purrr':
## 
##     pluck

## The following object is masked from 'package:readr':
## 
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## The following object is masked from 'package:XML':
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#display
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(rjson)    # Work with JSON format.
library(ggplot2)
library(purrr)
library(cowplot)  # ggplot2 add-on for pretty graphs.

## 
## ********************************************************

## Note: As of version 1.0.0, cowplot does not change the

##   default ggplot2 theme anymore. To recover the previous

##   behavior, execute:
##   theme_set(theme_cowplot())

## ********************************************************

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:lubridate':
## 
##     stamp

Proposal

Assessment of the state of science is a requirement for research. Assessment of intellectual property is a requirement for capitalization of knowledge for profit. This project explores tools for surveying patent data and research data in order to develop methods for making these assessments.

The project begins with an examination of data from NASA, whose open data portal includes a dataset of NASA patents. The format is a delimited text file. The data include a patent case number and title. Exploration of this dataset may include unsupervised learning to yield topics of patents.

The World Intellectual Property Organization (WIPO) publishes The WIPO Manual on Open Source Patent Analytics with instruction for data interrogation useful for subsequent parts of this project. The project will continue with a more detailed exploration of patent cases opened by NASA or another science-focused organization with a view to revealing more topics from within patent case data other than the title of the case, perhaps from the patent application, for example.

There are at least two data sources for patent data. The primary data source is the U.S. Patent and Trademark Office (USPTO), whose Open Data Portal publishes many datasets in various formats. USPTO also supplies APIs for patent data retrieval. Besides these APIs, there is at least one potentially useful third party API called PatentsView.

One resource for research publications is the Public Library of Science (PLOS), who publish open access peer reviewed articles. The inventory of articles seems limited, but appears to enable an introduction to searching for research papers for which we will utilize the R package, rplos, a programmatic interface to the Solr based search API provided by PLOS. Solr is an open source search platform built on Apache Lucerne.

Our research topic is quantification and characterization of NASA innovation through inspection of NASA’s patents. For example, we expect to count patents which cite NASA patents (quantification) and to mine topics from NASA patents (characterization). We will report and visualize these findings.

The motivation for this project relates to methods for Information Retrieval and the team’s collective interest and experience in startup and scientific industries. Efficient topic modelling can form the foundation of a number of advanced NLP techniques such as Word sense disambiguation, Natural Text Generation, and automatic Summarization on its own. A number of derivative analysis are possible based on NLP, including studies combining our results with industry or innovation metrics.

NASA Patents

• Data source: NASA’s Open Data Portal. Includes column specification.
• File: NASA_Patents.csv
• Download date: 11/14/2019

Data preparation notes

• Once you determine the data types of your columns,set the specifications and then use them for reading.
• readr blog post. Illustrates the approach, although this syntax doesn’t work anymore. The approach writes the specification to a file.
• readr - how to update col_spec object from spec(). Greg H.’s answer on Stack Overflow doesn’t require writing to a file. We use this approach.

nasa_patents_fn <- "NASA_Patents.csv"

# Specify datatypes.
nasa_spec <- spec_csv(file = file.path("data", nasa_patents_fn))

## Parsed with column specification:
## cols(
##   Center = col_character(),
##   Status = col_character(),
##   `Case Number` = col_character(),
##   `Patent Number` = col_character(),
##   `Application SN` = col_character(),
##   Title = col_character(),
##   `Patent Expiration Date` = col_character()
## )

nasa_spec$cols[["Status"]] <- col_factor()
nasa_spec$cols[["Patent Expiration Date"]] <- col_date("%m/%d/%Y")

# Read patent file.
nasa_patents <- readr::read_csv(file = file.path("data", nasa_patents_fn),
                                col_types = nasa_spec)

# In variable names, replace spaces.
names(nasa_patents) <- str_replace_all(string = names(nasa_patents),
                                       pattern = " ",
                                       replacement = "_")

# Transform the application number to USPTO format
nasa_patents <- nasa_patents %>%
  mutate(Application_SN_uspto = str_c("US",
        str_replace_all(Application_SN, "[[:punct:]]", "")))

Retrieve USPTO patent data

# Example https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=US12795356'
 
is_na_patnum <- is.na(nasa_patents$Patent_Number)
is_patnum <- nasa_patents$Patent_Number > 0

nasa_patent_numbers <- nasa_patents$Patent_Number[!is_na_patnum & is_patnum]


# Need to review contents of JSON files.
for (app in nasa_patents$Application_SN_uspto) {
  #get_patent_application_uspto(app)
  url <- paste0('https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=',app)
  path <- "data/uspto"
  file_path <- file.path(path, str_c(app, ".html"))

  if (!file.exists(file_path)) {
    patent_application <- jsonlite::fromJSON(url)
    #test
    print(patent_application)
    #  Bulk download not ideal
    ##download.file(patent_application$response$docs$archiveUrl, file.path(path)) 
    #  Instead use the following form to query for new HTML files 
    #  http://patft.uspto.gov/netahtml/PTO/index.html
    break
  }
  
}

## $response
## $response$numFound
## [1] 2
## 
## $response$start
## [1] 0
## 
## $response$docs
##   applicationType      documentId applicationNumber documentType
## 1         UTILITY US20110212334A1        US13033085  application
## 2         UTILITY     US8623253B2        US13033085        grant
##        publicationDate         documentDate       productionDate
## 1 2011-09-01T00:00:00Z 2011-09-01T00:00:00Z 2011-08-17T00:00:00Z
## 2 2014-01-07T00:00:00Z 2014-01-07T00:00:00Z 2013-12-24T00:00:00Z
##        applicationDate
## 1 2011-02-23T00:00:00Z
## 2 2011-02-23T00:00:00Z
##                                                                                      applicant
## 1 Jolley, Scott T., Gibson, Tracy L., Williams, Martha K., Parrish, Clyde F., Parks, Steven L.
## 2 Jolley, Scott T., Gibson, Tracy L., Williams, Martha K., Parrish, Clyde F., Parks, Steven L.
##                                                                                       inventor
## 1 Jolley, Scott T., Gibson, Tracy L., Williams, Martha K., Parrish, Clyde F., Parks, Steven L.
## 2 Jolley, Scott T., Gibson, Tracy L., Williams, Martha K., Parrish, Clyde F., Parks, Steven L.
##                                                                                                                assignee
## 1    United States of America as Represented by the Administrator of the National aeronautics and, Space Administration
## 2 The United States of America as Represented by the Administrator of the National Aeronautics and Space Administration
##                                                     title
## 1 Low-Melt Poly(amic Acids) and Polyimides and Their Uses
## 2 Low-Melt Poly(amic Acids) and Polyimides and Their Uses
##                                                                               archiveUrl
## 1 https://bulkdata.uspto.gov/data/patent/application/redbook/fulltext/2011/ipa110901.zip
## 2       https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/2014/ipg140107.zip
##      pdfPath year    _version_ patentNumber
## 1 NOTUPDATED 2011 1.622101e+18         <NA>
## 2 NOTUPDATED 2014 1.626650e+18      8623253

print("Downloads complete.")

## [1] "Downloads complete."

select relevant patent data from Google HTML

Exploratory chunk disabled.

# explore downloaded data
raw.html <- read_html("data/6020587.html")
# title
raw.html %>% html_nodes("body search-app article h1") %>% html_text() %>% .[[1]]
# filing date
raw.html %>% html_nodes("[itemprop='filingDate']") %>% html_text() %>% .[[2]]
# abstract
raw.html %>% html_nodes("abstract") %>% html_text()
# description > origin of the invention
raw.html %>% html_nodes("body search-app article section div div p") %>% html_text() %>% .[[1]]
# description > background of the invention > field of the invention
raw.html %>% html_nodes("body search-app article section div div p") %>% html_text() %>% .[[3]]
# cited by 
raw.html %>% html_nodes(xpath="/html/body/search-app/article/table") %>% html_table()
# cited by (count) nrow()
cited_by <- raw.html %>% html_nodes(xpath="/html/body/search-app/article/table") %>% html_table() 
#nrow(cited_by[[1]])
# patent citations 
raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/table") %>% html_table()
raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/h2") %>% html_text()
#filter(str_detect(raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/h2") %>% html_text(), "Patent Citations"))
#print(raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/table") %>% html_table())[[2]]

clean and transform selected data into dataframe

Executed, variables serialized, chunk disabled.

# create object with all filenames
files <- list.files(path="data", pattern="[0-9]{5,}.html", full.names=TRUE, recursive=FALSE)

# create lists of extracted data
# title
raw.title <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes("body search-app article h1") %>% html_text() %>% .[[1]] %>% str_remove(" \\n +\\- [[:alpha:]]{6,} [[:alpha:]]{6,}")
})
# abstract
raw.abstract <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes("abstract") %>% html_text()
})
# prior art date as proxy for filing date that includes support 1899 example
filing.date <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes("[itemprop='priorArtDate']") %>% html_text() %>% .[[1]]
})
# description > origin of the invention
raw.origin <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes("body search-app article section div div p") %>% html_text() %>% .[[1]]
})
# description > background of the invention > field of the invention
raw.background <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes("body search-app article section div div p") %>% html_text() %>% .[[3]]
})

section to answer question, “how many other patents does a patent reference (‘count.citations’), and how many other patents reference it (‘count.cited_by’)?” This question is a analagous to, “how many shoulders does a given patent stand on?”

todo: make sure it works across all dataframes; try incorporating Tommy’s snippet

todo: deuglify the regex

Executed, variables serialized, chunk disabled.

We observe that each patent includes citations to reference other patents, as well as those other patents that cite it. We then ask if it might be possible to identify counts of each to demonstrate, for instance: * a rough proxy metric of the referenciblity of each patent (how much more or less is a given patent cited?) * the count of citations over time

This analysis will help us begin to answer the question, “how, and to what extent, do patents build on other patents?” Subsequent analysis should account for patent density across time.

# cited by (count) 
cited_by <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/h2") %>% html_text() %>% str_subset(pattern = "Cited")
})

# clean output
cited_by <- as.character(cited_by)

# cited by (count) 
count.cited_by <- lapply(cited_by, function(x) 
  # remove up to digit
  x %>% str_remove("\\D+")
)
count.cited_by <- lapply(count.cited_by, function(x) 
  # remove after digit
  x %>% str_remove("\\)$")
)

count.cited_by <- lapply(count.cited_by, function(x) 
  # remove after digit
  x %>% as.integer()
)

# citations (count) 
citations <- lapply(files, function(x) {
  raw.html <- read_html(x, header=TRUE) # load file
  # apply function
  raw.html %>% html_nodes(xpath="/html/body/search-app/article/section/h2") %>% html_text() %>% str_subset(pattern = "Patent Citations")
})

# drop Non-Patent Citations
citations <- lapply(citations, function(x) {
  # apply function
  str_detect(x, "Non-") %>%
                  discard(x, .)
})

# clean output
citations <- as.character(citations)

# cited by (count) 
count.citations <- lapply(citations, function(x) 
  # remove up to digit
  x %>% str_remove("\\D+")
)
count.citations <- lapply(count.citations, function(x) 
  # remove after digit
  x %>% str_remove("\\)$")
)

count.citations <- lapply(count.citations, function(x) 
  # remove after digit
  x %>% as.integer()
)

clean text and output to df

Executed, variables serialized, chunk disabled.

# not necessary, but makes lists nicer to read
# todo: make consistent
raw.title <- unlist(raw.title, recursive = FALSE)
#raw.title
raw.abstract <- unlist(raw.abstract, recursive = FALSE)
raw.origin <- unlist(raw.origin, recursive = FALSE)
filing.date <- filing.date %>% unlist(recursive = FALSE)
raw.background <- unlist(raw.background, recursive = FALSE)
count.citations %>% unlist(recursive = FALSE)
count.cited_by %>% unlist(recursive = FALSE)

# get "appId" with regex
app_num <- unlist(str_extract_all(raw.title, "[A-Z]{2,}[0-9]{6,}[A-Z]+[0-9]?"))

# remove "appId" from "title" 
raw.title <- str_remove(raw.title, app_num)
#raw.title
raw.title <- str_remove(raw.title, " - ") # this could be less greedy
#raw.title

# bind lists into dataframe and name columns
raw.df <- setNames(do.call(rbind.data.frame, Map('c', app_num, raw.title, raw.abstract, raw.origin, raw.background, count.cited_by, count.citations, filing.date)), c("appId", "title", "abstract", "origin", "background", "citedBy", "citations", "filingDate"))
raw.df

write to file for easy access

Chunk disabled. Execute by hand if refreshing the CSV is necessary.

write.csv(raw.df,"data/patent_text.csv", row.names = FALSE)
saveRDS(count.citations, file.path("data", "count.citations.RDS"))
saveRDS(count.cited_by, file.path("data", "raw.html.RDS"))
saveRDS(raw.html, file.path("data", "count.cited_by.RDS"))
saveRDS(raw.df, file.path("data", "raw.df.RDS"))
saveRDS(app_num, file.path("data", "app_num.RDS"))
saveRDS(citations, file.path("data", "citations.RDS"))
saveRDS(cited_by, file.path("data", "cited_by.RDS"))
saveRDS(raw.abstract, file.path("data", "raw.abstract.RDS"))
saveRDS(raw.background, file.path("data", "raw.background.RDS"))
saveRDS(raw.origin, file.path("data", "raw.origin.RDS"))
saveRDS(raw.title, file.path("data", "raw.title.RDS"))
saveRDS(title, file.path("data", "title.RDS"))

read patent text

raw.df <- read.csv("data/patent_text.csv")

Explore and Transform the Data

Examine the patent text files and view the text sections. We note 932 observations, 8 variables, and 2 integer classes and 6 character classes.

patents <- read.csv("data/patent_text.csv", header = TRUE, stringsAsFactors = FALSE)
glimpse(patents)

## Observations: 932
## Variables: 8
## $ appId      <chr> "US464750A", "US5585083A", "US5606014A", "US5617873A"…
## $ title      <chr> "Button-hole-scissors gage  - Google Patents", "Catal…
## $ abstract   <chr> "Disclosed is a process for oxidizing formaldehyde to…
## $ origin     <chr> "(No Model.) A. I. CAMPBELL. ", "The invention descri…
## $ background <chr> "No. 464,750. Patented Dec.8,1891. ", "This invention…
## $ citedBy    <int> 4, 30, 20, 59, 140, 8, 47, 10, 19, 96, 17, 12, 39, 32…
## $ citations  <int> 0, 7, 3, 17, 2, 1, 9, 1, 11, 8, 21, 16, 37, 13, 3, 11…
## $ filingDate <chr> "1891-12-08", "1995-03-30", "1995-08-04", "1994-08-25…

kable(head(patents, n=2))

appId	title	abstract	origin	background	citedBy	citations	filingDate
US464750A	Button-hole-scissors gage - Google Patents	Disclosed is a process for oxidizing formaldehyde to carbon dioxide and water without the addition of energy. A mixture of formaldehyde and an oxidizing agent (e.g., ambient air containing formaldehyde) is exposed to a catalyst which includes a noble metal dispersed on a metal oxide which possesses more than one oxidation state. Especially good results are obtained when the noble metal is platinum, and the metal oxide which possesses more than one oxidation state is tin oxide. A promoter (i.e., a small amount of an oxide of a transition series metal) may be used in association with the tin oxide to provide very beneficial results.	(No Model.) A. I. CAMPBELL.	No. 464,750. Patented Dec.8,1891.	4	0	1891-12-08
US5585083A	Catalytic process for formaldehyde oxidation	Controlled molecular weight imide oligomers and co-oligomers containing pendent phenylethynyl groups (PEPIs) and endcapped with nonreactive or phenylethynyl groups have been prepared by the cyclodehydration of the precursor amide acid oligomers or co-oligomers containing pendent phenylethynyl groups and endcapped with nonreactive or phenylethynyl groups. The amine terminated amide acid oligomers or co-oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and diamine containing pendent phenylethynyl groups and subsequently endcapped with a phenylethynyl phthalic anhydride or monofunctional anhydride. The anhydride terminated amide acid oligomers and co-oligomers are prepared from the reaction of diamine(s) and diamine containing pendent phenylethynyl group(s) with an excess of dianhydride(s) and subsequently endcapped with a phenylethynyl amine or monofunctional amine. The polymerizations are carried out in polar aprotic solvents such as under nitrogen at room temperature. The amide acid oligomers or co-oligomers are subsequently cyclodehydrated to the corresponding imide oligomers. The polymers and copolymers prepared from these materials exhibit a unique and unexpected combination of properties.	The invention described herein was jointly made by employees of the United States Government, contract employees during the performance of work under a NASA contract which is subject to the provisions of Public Law 95-517 (35 USC 202) in which the contractor has elected not to retain title, and an employee of Rochester Gas and Electric Corporation during the performance of work under a Memorandum of Agreement.	This invention relates generally to oxidizing formaldehyde. It relates particularly to a process for oxidizing formaldehyde to carbon dioxide and water, which process includes exposing a gaseous mixture containing formaldehyde and an oxidizing agent to a catalyst of a noble metal dispersed on a metal oxide possessing more than one stable oxidation state.	30	7	1995-03-30

Data integrity

Check to make sure we are not operating on empty data. We are not.

nrow(patents) == sum(complete.cases(patents))

## [1] TRUE

Track our documents in case we need to make extra rows for each word

#patents <- patents %>%    
  #mutate(docId=as.integer(row.names(.)))
`%notin%` <- Negate(`%in%`)
if('docId' %notin% colnames(.)) patents<-add_column(patents, docId=as.integer(row.names(patents)), .before = 1)
head(patents, n=1)

##   docId     appId                                       title
## 1     1 US464750A Button-hole-scissors gage  - Google Patents
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             abstract
## 1 Disclosed is a process for oxidizing formaldehyde to carbon dioxide and water without the addition of energy. A mixture of formaldehyde and an oxidizing agent (e.g., ambient air containing formaldehyde) is exposed to a catalyst which includes a noble metal dispersed on a metal oxide which possesses more than one oxidation state. Especially good results are obtained when the noble metal is platinum, and the metal oxide which possesses more than one oxidation state is tin oxide. A promoter (i.e., a small amount of an oxide of a transition series metal) may be used in association with the tin oxide to provide very beneficial results.\n  
##                         origin                         background citedBy
## 1 (No Model.) A. I. CAMPBELL.  No. 464,750. Patented Dec.8,1891.        4
##   citations filingDate
## 1         0 1891-12-08

References 1. https://github.com/tidyverse/dplyr/issues/2047 2. https://tibble.tidyverse.org/reference/add_column.html 3. https://www.r-bloggers.com/the-notin-operator/

String-focused transformations

Remove third-party additions to text

head(patents$title, n=1)

## [1] "Button-hole-scissors gage  - Google Patents"

patents <- patents %>%
  #head(n=1) %>%
  mutate(title = str_remove_all(title, " - Google Patents"))
head(patents$title, n=1)

## [1] "Button-hole-scissors gage "

Titles and Abstracts

1. Extract document ids, titles and abstracts and convert to tibble in order to use tidy text methods
2. Replace common European characters into transliterated roman characters (e.g. â is a)
3. Replace upper with lower case characters
4. Tokenize the title and abstract variables into words
5. Remove default English stopword from those words
6. Normalize white-space: Remove leading, trailing and intermediate excess whitespace
7. Remove numbers Hold off on punctuation (contractions, dashes) and stemming until analysis, and because key terms are more sensitive to these changes
8. We note some words to remove after visualizing the text

patents <- patents %>%
  mutate(title=iconv(title, to='ASCII//TRANSLIT')) %>%
  mutate(title=tolower(title)) %>%
  mutate(title=str_replace_all(title,'\\w*\\d\\w*','')) %>%
  mutate(title=str_replace_all(title,'apparatus|article|control|device|include.*|provide.*|methods*|systems*','')) %>%
  mutate(abstract=iconv(abstract, to='ASCII//TRANSLIT')) %>%
  mutate(abstract=tolower(abstract)) %>%
  mutate(abstract=str_replace_all(abstract,'\\w*\\d\\w*','')) %>%
  mutate(abstract=str_replace_all(abstract,'apparatus|article|control|device|include.*|provide.*|methods*|systems*','')) %>%
  mutate_if(is.character, str_squish)
head(patents)

##   docId      appId
## 1     1  US464750A
## 2     2 US5585083A
## 3     3 US5606014A
## 4     4 US5617873A
## 5     5 US5632841A
## 6     6 US5640408A
##                                                                                             title
## 1                                                                       button-hole-scissors gage
## 2                                                    catalytic process for formaldehyde oxidation
## 3 imide oligomers and co-oligomers containing pendent phenylethynyl groups and polymers therefrom
## 4       non-invasive and for monitoring intracranial pressure and pressure volume index in humans
## 5                                   thin layer composite unimorph ferroelectric driver and sensor
## 6                                                                  quasi four-level tm:luag laser
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     abstract
## 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  disclosed is a process for oxidizing formaldehyde to carbon dioxide and water without the addition of energy. a mixture of formaldehyde and an oxidizing agent (e.g., ambient air containing formaldehyde) is exposed to a catalyst which
## 2 led molecular weight imide oligomers and co-oligomers containing pendent phenylethynyl groups (pepis) and endcapped with nonreactive or phenylethynyl groups have been prepared by the cyclodehydration of the precursor amide acid oligomers or co-oligomers containing pendent phenylethynyl groups and endcapped with nonreactive or phenylethynyl groups. the amine terminated amide acid oligomers or co-oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and diamine containing pendent phenylethynyl groups and subsequently endcapped with a phenylethynyl phthalic anhydride or monofunctional anhydride. the anhydride terminated amide acid oligomers and co-oligomers are prepared from the reaction of diamine(s) and diamine containing pendent phenylethynyl group(s) with an excess of dianhydride(s) and subsequently endcapped with a phenylethynyl amine or monofunctional amine. the polymerizations are carried out in polar aprotic solvents such as under nitrogen at room temperature. the amide acid oligomers or co-oligomers are subsequently cyclodehydrated to the corresponding imide oligomers. the polymers and copolymers prepared from these materials exhibit a unique and unexpected combination of properties.
## 3                                                                                                                                                                                                                    non-invasive measuring s responsive to changes in a patient's intracranial pressure (icp) can be accurately calibrated for monitoring purposes by providing known changes in icp by non-invasive , such as placing the patient on a tilting bed and calculating a change in icp from the tilt angle and the length of the patient's cerebrospinal column, or by placing a pressurized skull cap on the patient and measuring the inflation pressure. absolute values for the patient's pressure-volume index (pvi) and the steady state icp can then be determined by inducing two known changes in the volume of cerebrospinal fluid while recording the corresponding changes in icp by means of the calibrated measuring . the two pairs of data for pressure change and volume change are entered into an equation developed from an equation describing the relationship between icp and cerebrospinal fluid volume. pvi and steady state icp are then determined by solving the equation. for inducing known changes in cerebrospinal fluid volume are described.
## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      a for forming ferroelectric wafers is
## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    a quasi four-level solid-state laser is
## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             a and for detecting optimum protein crystallization conditions and for growing protein crystals in either or microgravity environments comprising a housing defining at least one pair of chambers for containing crystallization solutions. the housing further defines an orifice therein for providing fluid communication between the chambers. the orifice is adapted to receive a tube which contains a gelling substance for limiting the rate of diffusive mixing of the crystallization solutions. the solutions are diffusively mixed over a period of time defined by the quantity of gelling substance sufficient to achieve equilibration and to substantially reduce density driven convection disturbances therein. the further
##                                                                                                                                                                                                                                                                                                                                                                                                                          origin
## 1                                                                                                                                                                                                                                                                                                                                                                                                   (No Model.) A. I. CAMPBELL.
## 2 The invention described herein was jointly made by employees of the United States Government, contract employees during the performance of work under a NASA contract which is subject to the provisions of Public Law 95-517 (35 USC 202) in which the contractor has elected not to retain title, and an employee of Rochester Gas and Electric Corporation during the performance of work under a Memorandum of Agreement.
## 3                                                                                                                                                                                                   This invention described herein was made by employees of the United States Government and may be manufactured and used by or for the Government or government purposes without payment of any royalties therein or thereof.
## 4                                                                                                                                                                     The invention described herein was made in the performance of work done by employees of the U.S. Government and may be manufactured and used by or for the government for governmental purposes without the payment of any royalties thereon or therefor.
## 5                                                                                                                                                                                                            The invention described herein was made by employees of the United States Government and may be used by and for the Government for governmental purposes without the payment of any royalties thereon or therefor.
## 6                                                                                                                                                                                                                                                                               This invention was made with Government support under contract NAS1-19603 awarded by NASA. The Government has certain rights in this invention.
##                                                                                                                                                                                                                                                                                                                                                             background
## 1                                                                                                                                                                                                                                                                                                                                    No. 464,750. Patented Dec.8,1891.
## 2 This invention relates generally to oxidizing formaldehyde. It relates particularly to a process for oxidizing formaldehyde to carbon dioxide and water, which process includes exposing a gaseous mixture containing formaldehyde and an oxidizing agent to a catalyst of a noble metal dispersed on a metal oxide possessing more than one stable oxidation state.
## 3                                              The synthesis and characterization of PI has been extensively studied and documented. Reviews on PI are available. [J. W. Verbicky, Jr., "Polyimides" in Encyclopedia of Polymer Science and Engineering, 2nd Ed., John Wiley and Sons, New York, Vol. 12, 364 (1988); C. E. Sroog, Prog. Polym. Sci., 16, 591 (1991)].
## 4                                                                                                                                                                                                                                                                                                                                            1. Field of The invention
## 5                                                                                                                                                                                                    The present invention relates generally to ferroelectric devices, and more particularly to ferroelectric devices providing large mechanical output displacements.
## 6                                                                                                                                                                                                             The invention relates generally to lasers, and more particularly to quasi four-level lasers operating at room temperature in the 2 μm wavelength region.
##   citedBy citations filingDate
## 1       4         0 1891-12-08
## 2      30         7 1995-03-30
## 3      20         3 1995-08-04
## 4      59        17 1994-08-25
## 5     140         2 1995-04-04
## 6       8         1 1996-01-29

Data-focused transformations

Character statistics

patents_stats <- patents %>% 
  rowwise() %>% 
  transmute(title = title, title_nchar = nchar(title), title_nmeans= nchar(title),
            abstract=abstract, abstract_nchar = nchar(abstract), abstract_nmeans= nchar(abstract),
            origin = origin, origin_nchar = nchar(origin), origin_nmeans= nchar(origin),
            background = background, background_nchar = nchar(background), background_nmeans= nchar(background)) %>%
  mutate(title_nmeans=colMeans(.[3]), 
         abs_nmeans=colMeans(.[6]), 
         origin_nmeans=colMeans(.[9]), 
         background_nmeans=colMeans(.[12])) %>%
  cbind(select(patents, -c(title, abstract, origin, background)),.)
head(patents_stats, n=2)

##   docId      appId citedBy citations filingDate
## 1     1  US464750A       4         0 1891-12-08
## 2     2 US5585083A      30         7 1995-03-30
##                                          title title_nchar title_nmeans
## 1                    button-hole-scissors gage          25           NA
## 2 catalytic process for formaldehyde oxidation          44           NA
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     abstract
## 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  disclosed is a process for oxidizing formaldehyde to carbon dioxide and water without the addition of energy. a mixture of formaldehyde and an oxidizing agent (e.g., ambient air containing formaldehyde) is exposed to a catalyst which
## 2 led molecular weight imide oligomers and co-oligomers containing pendent phenylethynyl groups (pepis) and endcapped with nonreactive or phenylethynyl groups have been prepared by the cyclodehydration of the precursor amide acid oligomers or co-oligomers containing pendent phenylethynyl groups and endcapped with nonreactive or phenylethynyl groups. the amine terminated amide acid oligomers or co-oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and diamine containing pendent phenylethynyl groups and subsequently endcapped with a phenylethynyl phthalic anhydride or monofunctional anhydride. the anhydride terminated amide acid oligomers and co-oligomers are prepared from the reaction of diamine(s) and diamine containing pendent phenylethynyl group(s) with an excess of dianhydride(s) and subsequently endcapped with a phenylethynyl amine or monofunctional amine. the polymerizations are carried out in polar aprotic solvents such as under nitrogen at room temperature. the amide acid oligomers or co-oligomers are subsequently cyclodehydrated to the corresponding imide oligomers. the polymers and copolymers prepared from these materials exhibit a unique and unexpected combination of properties.
##   abstract_nchar abstract_nmeans
## 1            233             233
## 2           1242            1242
##                                                                                                                                                                                                                                                                                                                                                                                                                          origin
## 1                                                                                                                                                                                                                                                                                                                                                                                                   (No Model.) A. I. CAMPBELL.
## 2 The invention described herein was jointly made by employees of the United States Government, contract employees during the performance of work under a NASA contract which is subject to the provisions of Public Law 95-517 (35 USC 202) in which the contractor has elected not to retain title, and an employee of Rochester Gas and Electric Corporation during the performance of work under a Memorandum of Agreement.
##   origin_nchar origin_nmeans
## 1           27      242.8637
## 2          413      242.8637
##                                                                                                                                                                                                                                                                                                                                                             background
## 1                                                                                                                                                                                                                                                                                                                                    No. 464,750. Patented Dec.8,1891.
## 2 This invention relates generally to oxidizing formaldehyde. It relates particularly to a process for oxidizing formaldehyde to carbon dioxide and water, which process includes exposing a gaseous mixture containing formaldehyde and an oxidizing agent to a catalyst of a noble metal dispersed on a metal oxide possessing more than one stable oxidation state.
##   background_nchar background_nmeans abs_nmeans
## 1               33          368.5944         NA
## 2              356          368.5944         NA

Dates

Store the dates in date format yyyy-mm-dd.

patents_stats <- patents_stats %>%
  rowwise() %>%
  mutate(filingDate=ymd(filingDate))
head(patents_stats, n=1)

## Source: local data frame [1 x 18]
## Groups: <by row>
## 
## # A tibble: 1 x 18
##   docId appId citedBy citations filingDate title title_nchar title_nmeans
##   <int> <chr>   <int>     <int> <date>     <chr>       <int>        <dbl>
## 1     1 US46…       4         0 1891-12-08 butt…          25           NA
## # … with 10 more variables: abstract <chr>, abstract_nchar <int>,
## #   abstract_nmeans <int>, origin <chr>, origin_nchar <int>,
## #   origin_nmeans <dbl>, background <chr>, background_nchar <int>,
## #   background_nmeans <dbl>, abs_nmeans <dbl>

Final Data Definitions

docId: A unique patent document data record in our sample (NASA) appId: A unique patent number ‘r’ citedBy: Other papers citing this paper, provided by Google citation: Number of other papers this paper cites filingDate: The date the patent was filed in yyyy-mm-dd format (for instance a specific version) title: The patent title with the prepended phrase " - Google Patents" removed title_nchar [patent_stats]: the number of characters in the title title_nmeans [patent_stats]: the average number of characters over all of the titles in all patents abstract: a summary of the patent abstract abstract_nchar: the number of characters in the abstract abstract_nmeans: the average number of characters over all of the abstracts in all patents origin: a summary of how the patent originated origin_nchar [patent_stats]: the number of characters in the origin origin_nmeans [patent_stats]: the average number of characters over all of the origins in all patents background: a summary of the patent background background_nchar [patent_stats]: the number of characters in the background background_nmeans [patent_stats]: the average number of characters over all of the backgrounds in all patents

References: 1. https://dplyr.tidyverse.org/reference/mutate.html#grouped-tibbles 2. https://dplyr.tidyverse.org/reference/summarise.html 3. https://github.com/tidyverse/dplyr/issues/2838 4. https://stackoverflow.com/questions/43897844/r-move-column-to-last-using-dplyr

Data Analysis

After looking into the patent stats, and transforming the data, we choose title and abstract for our Data Analysis.

Perform Word Tokenization specific to our type of analysis

Title word operations

tb_titles <- patents %>%
  select(., c(docId, title)) %>%
  as_tibble() %>%
  unnest_tokens(word, title) %>%
  anti_join(stop_words)

Abstract word operations

tb_abstracts <- patents %>%
  select(., c(docId, abstract)) %>%
  as_tibble() %>%
  unnest_tokens(word, abstract) %>%
  anti_join(stop_words)

Show Basic Word statistics

1. Note the most frequent words over all patents
2. Note the most frequent words per patent

Most frequent words in titles

title_words <- tb_titles %>%
  count(word, sort = TRUE)

title_words %>%
  top_n(10) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n, fill=word)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  ylab("Word Frequency") +
  ggtitle("Most Common Title Words") 

#head(title_words, 5)

Most frequent words in each title

title_word_counts <- tb_titles %>%
  count(docId, word, sort = TRUE)%>%
  ungroup()

head(title_word_counts, 5)

## # A tibble: 5 x 3
##   docId word          n
##   <int> <chr>     <int>
## 1   203 ester         3
## 2   409 ester         3
## 3   711 flow          3
## 4     3 oligomers     2
## 5     4 pressure      2

Most frequent words in Abstracts

abstract_words <- tb_abstracts %>%
  count(word, sort = TRUE)

abstract_words %>%
  top_n(10) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n, fill=word)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  ylab("Word Frequency") +
  ggtitle("Most Common Abstract Words") 

#head(abstract_words, 5)

Most frequent words in each Abstract

abstract_word_counts <- tb_abstracts %>%
  count(docId, word, sort = TRUE)%>%
  ungroup()

head(abstract_word_counts, 5)

## # A tibble: 5 x 3
##   docId word        n
##   <int> <chr>   <int>
## 1   702 image      19
## 2   553 optical    17
## 3   787 flight     16
## 4   879 beam       16
## 5   879 sample     13

References: 1. https://stackoverflow.com/questions/20495598/replace-accented-characters-in-r-with-non-accented-counterpart-utf-8-encoding 2. https://www.tidytextmining.com/tidytext.html

Cast to Document Term Matrix

Title DTM

title_dtm <- title_word_counts %>%
  cast_dtm(docId, word, n)

title_dtm

## <<DocumentTermMatrix (documents: 932, terms: 1969)>>
## Non-/sparse entries: 4984/1830124
## Sparsity           : 100%
## Maximal term length: NA
## Weighting          : term frequency (tf)

Abstract DTM

abstract_dtm <- abstract_word_counts %>%
  cast_dtm(docId, word, n)

abstract_dtm

## <<DocumentTermMatrix (documents: 878, terms: 5030)>>
## Non-/sparse entries: 18952/4397388
## Sparsity           : 100%
## Maximal term length: NA
## Weighting          : term frequency (tf)

Perform Abstract LDA and Visualize

Build the LDA

#Disable to avoid unintentional overwrites
abstract_lda <- LDA(abstract_dtm, k = 24, control = list(seed = 1234))
abstract_lda

## A LDA_VEM topic model with 24 topics.

#Free memory
gc(abstract_dtm)

##           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
## Ncells 2443951 130.6    4197124 224.2         NA  4197124 224.2
## Vcells 4885779  37.3   10146329  77.5      32768  8388547  64.0

gc(tb_titles)

##           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
## Ncells 2443884 130.6    4197124 224.2         NA  4197124 224.2
## Vcells 4885676  37.3   10146329  77.5      32768  8388547  64.0

gc(tb_abstracts) 

##           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
## Ncells 2443892 130.6    4197124 224.2         NA  4197124 224.2
## Vcells 4885707  37.3   10146329  77.5      32768  8388547  64.0

Arrange the LDA and Visualize top words per topic

tidy_abstract_lda <- tidy(abstract_lda)

top_abstract_terms <- tidy_abstract_lda %>%
  group_by(topic) %>%
  top_n(10, beta) %>%
  ungroup() %>%
  arrange(topic, -beta)

top_abstract_terms %>%
  mutate(term = reorder_within(term, beta, topic)) %>%
  group_by(topic, term) %>%    
  arrange(desc(beta)) %>%  
  ungroup() %>%
  ggplot(aes(term, beta, fill = as.factor(topic))) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  scale_x_reordered() +
  labs(title = "Top 10 terms in each LDA topic",
       x = NULL, y = expression(beta)) +
  facet_wrap(~ topic, ncol = 4, scales = "free")

Review performance of Abstract LDA per document

Our LDA shows an even distribution of probability across many documents.

lda_gamma <- tidy(abstract_lda, matrix = "gamma")

ggplot(lda_gamma, aes(gamma)) +
  geom_histogram() +
  scale_y_log10() +
  labs(title = "Distribution of probabilities for all topics",
       y = "Number of documents", x = expression(gamma))

ggplot(lda_gamma, aes(gamma, fill = as.factor(topic))) +
  geom_histogram(show.legend = FALSE) +
  facet_wrap(~ topic, ncol = 4) +
  scale_y_log10() +
  labs(title = "Distribution of probability for each topic",
       y = "Number of documents", x = expression(gamma))

Experimenting with LDA

Trying another strategy, we can boost the number of topics and then extract the top term per document. Other strategies include creating bi- or tri-grams or combining TF-IDF and other Keywords such as those available in tidytextmining from NASA with the LDA topics or associated words.

Here we try to increase the topics to 100 (roughly 1/8 of the documents).

gc(abstract_lda)

##           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
## Ncells 2468653 131.9    4197124 224.2         NA  4197124 224.2
## Vcells 5278529  40.3   10146329  77.5      32768  9298871  71.0

abstract_lda <- LDA(abstract_dtm, k = 100, control = list(seed = 1234))
abstract_lda

## A LDA_VEM topic model with 100 topics.

#Fix———– ### Find the top one term per LDA topic

topics <- tidy(abstract_lda, matrix = "beta") 
topics %>%  
  mutate(term = reorder(term, beta)) %>% 
  # Need to limit number of terms 
  group_by(term) %>%
  top_n(3, beta) %>%
  ungroup() %>%
  ggplot(aes(term, beta, fill = factor(topic))) +  
  geom_col(show.legend = FALSE) +  
  facet_wrap(~ topic, scales = "free") +  
  labs(x = NULL, y = "Beta") +  
  coord_flip()  

abs_topics_term <- topics %>%
  group_by(topic) %>% 
  filter(beta == max(beta)) %>% 
  slice(1) %>% 
  ungroup() 
head(abs_topics_term,n=5)

## # A tibble: 5 x 3
##   topic term        beta
##   <int> <chr>      <dbl>
## 1     1 circuit   0.0537
## 2     2 region    0.0380
## 3     3 signal    0.0425
## 4     4 selected  0.0464
## 5     5 partition 0.0293

Merge with dataset and extract to csv

Merge

Get one topic per document

abs_topic_docs <- tidy(abstract_lda, matrix = "gamma")
topics_ <- abs_topic_docs %>% 
  mutate(docId=as.integer(document)) %>%
  select(., -document) %>% 
  group_by(docId) %>% 
  filter(gamma == max(gamma)) %>% 
  slice(1) %>% 
  ungroup()

Get one term per topic

terms_ <- subset(abs_topics_term, topic==topics_$docId, -beta)  

## Warning in topic == topics_$docId: longer object length is not a multiple
## of shorter object length

## Warning: Length of logical index must be 1 or 100, not 878

topic_terms <- inner_join(topics_, terms_, by = "topic")

Check for errors

# Rewrite
any(is.na(topic_terms))

## [1] FALSE

Merge to patent document df

patent_topics <- merge(patents, topic_terms, by = "docId") %>% 
  group_by(docId) %>% arrange(., docId)
head(patent_topics)

## # A tibble: 6 x 12
## # Groups:   docId [6]
##   docId appId title abstract origin background citedBy citations filingDate
##   <int> <chr> <chr> <chr>    <chr>  <chr>        <int>     <int> <chr>     
## 1     5 US56… thin… a quasi… The i… The prese…     140         2 1995-04-04
## 2     8 US56… diam… a and f… This … Polyimide…      10         1 1995-08-04
## 3    15 US57… toug… this in… The i… The prese…      16         3 1994-12-16
## 4    48 US61… rese… a polyi… The i… In genera…      11         8 1998-12-09
## 5    60 US62… for … disclos… This … The prese…      98        18 1996-05-10
## 6    66 US63… actu… a and f… The i… The prese…      16        35 1996-12-26
## # … with 3 more variables: topic <int>, gamma <dbl>, term <chr>

Extract

#write.csv(patents_topic_terms,"data/patents_topic_terms.csv")

#————End fix

Reference: 1. https://www.tidytextmining.com/tidytext.html 2. https://www.kaggle.com/rtatman/nlp-in-r-topic-modelling#Unsupervised-topic-modeling-with-LDA

TF-IDF

Measures the importance of a word within the corpus, potentially surfacing not unique words to the document (tf) but those that are not very frequent in the corpus (IDF). We investigate this potentially more reliable method (given the small dataset (corpus)) until we are able to expand or augment the LDA topic analysis and then test it.

Visualize the top TD-IDF words in the corpus

abstracts_tf_idf <- tb_abstracts %>% 
  count(docId, word, sort = TRUE) %>%
  ungroup() %>%
  bind_tf_idf(word, docId, n) 

 
abstracts_tf_idf %>% 
  top_n(10) %>%
  ggplot(aes(word, tf_idf, fill=word)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  ylab("Word TF-IDF") +
  ggtitle("Sample of Most Relevant, Unique Abstract Words in Corpus") 

Find the top one TF-IDF term per document

doc_abs_tf_idf <- abstracts_tf_idf %>%
  group_by(docId) %>% 
  filter(tf_idf == max(tf_idf)) %>% 
  slice(1) %>% 
  ungroup() 
head(doc_abs_tf_idf,n=5)

## # A tibble: 5 x 6
##   docId word              n     tf   idf tf_idf
##   <int> <chr>         <int>  <dbl> <dbl>  <dbl>
## 1     1 formaldehyde      3 0.158   6.08  0.961
## 2     2 oligomers        11 0.124   5.17  0.639
## 3     3 icp               7 0.0875  5.39  0.472
## 4     4 ferroelectric     1 0.333   5.39  1.80 
## 5     5 quasi             1 0.25    5.68  1.42

Merge with dataset and extract to csv

Merge

doc_terms <- doc_abs_tf_idf %>% transmute(docId=docId, term=word) 
patents_terms <- merge(doc_terms, patents, by='docId')

kable(head(patents_terms, n=2))

docId	term	appId	title	abstract	origin	background	citedBy	citations	filingDate
1	formaldehyde	US464750A	button-hole-scissors gage	disclosed is a process for oxidizing formaldehyde to carbon dioxide and water without the addition of energy. a mixture of formaldehyde and an oxidizing agent (e.g., ambient air containing formaldehyde) is exposed to a catalyst which	(No Model.) A. I. CAMPBELL.	No. 464,750. Patented Dec.8,1891.	4	0	1891-12-08
2	oligomers	US5585083A	catalytic process for formaldehyde oxidation	led molecular weight imide oligomers and co-oligomers containing pendent phenylethynyl groups (pepis) and endcapped with nonreactive or phenylethynyl groups have been prepared by the cyclodehydration of the precursor amide acid oligomers or co-oligomers containing pendent phenylethynyl groups and endcapped with nonreactive or phenylethynyl groups. the amine terminated amide acid oligomers or co-oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and diamine containing pendent phenylethynyl groups and subsequently endcapped with a phenylethynyl phthalic anhydride or monofunctional anhydride. the anhydride terminated amide acid oligomers and co-oligomers are prepared from the reaction of diamine(s) and diamine containing pendent phenylethynyl group(s) with an excess of dianhydride(s) and subsequently endcapped with a phenylethynyl amine or monofunctional amine. the polymerizations are carried out in polar aprotic solvents such as under nitrogen at room temperature. the amide acid oligomers or co-oligomers are subsequently cyclodehydrated to the corresponding imide oligomers. the polymers and copolymers prepared from these materials exhibit a unique and unexpected combination of properties.	The invention described herein was jointly made by employees of the United States Government, contract employees during the performance of work under a NASA contract which is subject to the provisions of Public Law 95-517 (35 USC 202) in which the contractor has elected not to retain title, and an employee of Rochester Gas and Electric Corporation during the performance of work under a Memorandum of Agreement.	This invention relates generally to oxidizing formaldehyde. It relates particularly to a process for oxidizing formaldehyde to carbon dioxide and water, which process includes exposing a gaseous mixture containing formaldehyde and an oxidizing agent to a catalyst of a noble metal dispersed on a metal oxide possessing more than one stable oxidation state.	30	7	1995-03-30

Extract

write.csv(patents_terms,"data/patents_terms.csv")

Reference: 1. https://www.tidytextmining.com/tidytext.html

Appendices

Retrieve USPTO patent data

The code chunk for the USPTO acquistion shall be used when documents beyond the Google archive are needed.

library(jsonlite)

# Example https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=US12795356'
 
# Requires nasa_patents objects

for (app in nasa_patents$Application_SN_uspto) {
  #get_patent_application_uspto(app)
  url <- paste0('https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=',app)
  path <- "data/uspto"
  file_path <- file.path(path, str_c(app, ".html"))
raw.df <- read.csv("data/patent_text.csv", as.is = TRUE)
raw.df$filingDate <- as.Date(raw.df$filingDate, format = "%Y-%m-%d")
}

EDA and data transformation for visualization

# drop outlier date 
raw.df <- raw.df %>% 
  subset(filingDate != as.Date("1891-12-08"))

# date quartiles 
quartile <- (max(raw.df$filingDate) - min(raw.df$filingDate)) / 4 
q1 <- min(raw.df$filingDate) + quartile
q2 <- min(raw.df$filingDate) + (quartile*2)
q3 <- min(raw.df$filingDate) + (quartile*3)
q4 <- max(raw.df$filingDate)

# citation count per patent filed between 2006 and 2012
citations4 <- raw.df %>% 
  select(citations, citedBy, title, filingDate) %>%
  filter(filingDate >= as.Date(q3) & filingDate <= as.Date(q4))

# citation count per patent filed between 2000 and 2006
citations3 <- raw.df %>% 
  select(citations, citedBy, title, filingDate) %>%
  filter(filingDate >= as.Date(q2) & filingDate <= as.Date(q3))

# citation count per patent filed between 1994 and 2000
citations2 <- raw.df %>% 
  select(citations, citedBy, title, filingDate) %>%
  filter(filingDate >= as.Date(q1) & filingDate <= as.Date(q2))

# citation count per patent filed before 1994
citations1 <- raw.df %>% 
  select(citations, citedBy, title, filingDate) %>%
  filter(filingDate <= as.Date(q1))

# patent density -- which years most patents?
ggplot(raw.df, aes(x=filingDate)) + 
  geom_histogram(aes(y=..density..), colour="black", fill="white") +
  geom_density(alpha=.2, fill="#FF6666") +
  ggtitle("Patent density by year") +
  xlab("year") + ylab("density")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# citations and citedBy count over all years
c1 <- ggplot(raw.df, aes(x=filingDate, y=citedBy)) + 
  geom_point() +
  ggtitle("(n) cited by other patents by year") +
  xlab("year") + ylab("cited by")
c2 <- ggplot(raw.df, aes(x=filingDate, y=citations)) + 
  geom_point() +
  ggtitle("(n) citations by year") +
  xlab("year") + ylab("citations")
plot_grid(c1, c2, labels = c('A', 'B'), label_size = 12)

# citations count by year quartiles (zoomed in)
p1 <- ggplot(citations1, aes(x=filingDate, y=citations)) + 
  geom_point() +
  #stat_summary(fun.data=mean_cl_normal) + 
  #geom_smooth(method='lm', formula= y~x) +
  ggtitle("(n) citation counts, 2006-2012") +
  xlab("year") + ylab("citations")
p2 <- ggplot(citations2, aes(x=filingDate, y=citations)) + 
  geom_point() +
  #stat_summary(fun.data=mean_cl_normal) + 
  #geom_smooth(method='lm', formula= y~x) +
  ggtitle("(n) citation counts, 2000-2006") +
  xlab("year") + ylab("citations")
p3 <- ggplot(citations3, aes(x=filingDate, y=citations)) + 
  geom_point() +
  #stat_summary(fun.data=mean_cl_normal) + 
  #geom_smooth(method='lm', formula= y~x) +
  ggtitle("(n) citation counts, 1994-2000") +
  xlab("year") + ylab("citations")
p4 <- ggplot(citations4, aes(x=filingDate, y=citations)) + 
  geom_point() +
  #stat_summary(fun.data=mean_cl_normal) + 
  #geom_smooth(method='lm', formula= y~x) +
  ggtitle("(n) citation counts, pre-1994") +
  xlab("year") + ylab("citations")
plot_grid(p1, p2, p3, p4, labels = c('A', 'B', 'C', 'D'), label_size = 12)

Findings

NASA patents appear to have peaked in count between 2005 and 2008.

Appendix

USPTO

We didn’t use the USPTO source. We acquired patent data from Google, instead. This code chunk for this acquistion is disabled because I don’t understand what it retrieves, but I’ll keep the code in case I can use it later.

# Curl
# curl -X GET --header 'Accept: application/json' 'https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=US14202699%2CUS12795356&start=0&rows=100'
# Request URL
# https://developer.uspto.gov/ibd-api/v1/patent/application?applicationNumber=US14202699%2CUS12795356&start=0&rows=100

  if (!file.exists(file_path)) {
    patent_application <- jsonlite::fromJSON(url)
    #test
    print(patent_application)
    
    #  Bulk download not ideal, disable download from USPTO
    ##download.file(patent_application$response$docs$archiveUrl, file.path(path)) 
    
    #  Instead use the following form to query for new HTML files 
    #  http://patft.uspto.gov/netahtml/PTO/index.html
    #  Example: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=5963949.PN.&OS=PN/5963949&RS=PN/5963949
    break
  }
  
}

print("Downloads complete.")

View Patents View Information (for instance, the Abstract)

library(patentsview) 

retrvble_flds <- get_fields(endpoint = "patents")
head(retrvble_flds)

## [1] "app_country"       "app_date"          "app_number"       
## [4] "app_type"          "appcit_app_number" "appcit_category"

result <- search_pv(
  query = qry_funs$eq(patent_number = "8293178"),
  fields = get_fields(endpoint = "patents", groups = c("patents", "inventors"))
)

result

## $data
## #### A list with a single data frame (with list column(s) inside) on a patent level:
## 
## List of 1
##  $ patents:'data.frame': 1 obs. of  32 variables:
##   ..$ detail_desc_length                    : chr "21556"
##   ..$ patent_abstract                       : chr "A chemochromic sensor"..
##   ..$ patent_average_processing_time        : chr "1590"
##   ..$ patent_date                           : chr "2012-10-23"
##   ..$ patent_firstnamed_assignee_city       : chr "Washington"
##   ..$ patent_firstnamed_assignee_country    : chr "US"
##   ..$ patent_firstnamed_assignee_id         : chr "org_EolmLkaBf9MsnLD1f"..
##   ..$ patent_firstnamed_assignee_latitude   : chr "38.895"
##   ..$ patent_firstnamed_assignee_location_id: chr "38.895|-77.0367"
##   ..$ patent_firstnamed_assignee_longitude  : chr "-77.0367"
##   ..$ patent_firstnamed_assignee_state      : chr "DC"
##   ..$ patent_firstnamed_inventor_city       : chr "Titusville"
##   ..$ patent_firstnamed_inventor_country    : chr "US"
##   ..$ patent_firstnamed_inventor_id         : chr "7790787-4"
##   ..$ patent_firstnamed_inventor_latitude   : chr "28.6119"
##   ..$ patent_firstnamed_inventor_location_id: chr "28.6119|-80.8078"
##   ..$ patent_firstnamed_inventor_longitude  : chr "-80.8078"
##   ..$ patent_firstnamed_inventor_state      : chr "FL"
##   ..$ patent_id                             : chr "8293178"
##   ..$ patent_kind                           : chr "B2"
##   ..$ patent_num_cited_by_us_patents        : chr "0"
##   ..$ patent_num_claims                     : chr "14"
##   ..$ patent_num_combined_citations         : chr "10"
##   ..$ patent_num_foreign_citations          : chr "0"
##   ..$ patent_num_us_application_citations   : chr "5"
##   ..$ patent_num_us_patent_citations        : chr "5"
##   ..$ patent_number                         : chr "8293178"
##   ..$ patent_processing_time                : chr "1813"
##   ..$ patent_title                          : chr "Chemochromic detector"..
##   ..$ patent_type                           : chr "utility"
##   ..$ patent_year                           : chr "2012"
##   ..$ inventors                             :List of 1
## 
## $query_results
## #### Distinct entity counts across all downloadable pages of output:
## 
## total_patent_count = 1

result$data$patents$patent_abstract

## [1] "A chemochromic sensor for detecting a combustible gas, such as hydrogen, includes a chemochromic pigment mechanically mixed with a polymer and formed into a rigid or pliable material. In a preferred embodiment, the chemochromic detector includes aerogel material. The detector is robust and easily modifiable for a variety of applications and environmental conditions, such as atmospheres of inert gas, hydrogen gas, or mixtures of gases, or in environments that have variable temperature, including high temperatures such as above 100° C. and low temperatures such as below −196° C."

Google Patents

Other attributes explored

# prior art keywords
raw.html %>% html_nodes("[itemprop='priorArtKeywords']") %>% html_text() 
# inventors
raw.html %>% html_nodes("[itemprop='inventor']") %>% html_text()
# current assignee
raw.html %>% html_nodes("[itemprop='assigneeCurrent']") %>% html_text()
# original assignee
#raw.html %>% html_nodes("[itemprop='assigneeOriginal']") %>% html_text() # lots of noise; need to parse later
# filing date
raw.html %>% html_nodes("[itemprop='filingDate']") %>% html_text() # which one to select?
# classifications
raw.html %>% html_nodes("[itemprop='cpcs']") %>% html_text() # what does it mean?

Research papers

Explore PLOS. Commentary in code chunk. Unhide to review.

# All the fields you can query.
plosfields

##                            field                     description
## 10                      abstract                Abstract section
## 49                abstract_ngram                            <NA>
## 11      abstract_primary_display                Abstract section
## 21                 accepted_date                   Accepted Date
## 38                     affiliate                       Affiliate
## 50               affiliate_facet                            <NA>
## 5                alternate_title               Alternative Title
## 25                  article_type                    Article Type
## 51            article_type_facet                            <NA>
## 6                         author                          Author
## 52              author_affiliate                            <NA>
## 9     author_collab_only_display                          Author
## 7                 author_display                          Author
## 53                  author_facet                            <NA>
## 39                  author_notes                    Author Notes
## 8  author_without_collab_display                          Author
## 18                          body    Most sections of the article
## 54                    body_ngram                            <NA>
## 55                      body_rev                            <NA>
## 40            competing_interest    Competing Interest Statement
## 15                   conclusions             Conclusions section
## 45                     copyright             copyright-statement
## 42             counter_total_all           Total views, all time
## 43           counter_total_month       Total views, last 30 days
## 48 cross_published_journal_eissn                  no description
## 47   cross_published_journal_key     Cross Published Journal Key
## 46  cross_published_journal_name    Cross Published Journal Name
## 56              doc_partial_body                            <NA>
## 57         doc_partial_parent_id                            <NA>
## 58              doc_partial_type                            <NA>
## 59                      doc_type                            <NA>
## 36                        editor                          Editor
## 60              editor_affiliate                            <NA>
## 37                editor_display                          Editor
## 61                  editor_facet                            <NA>
## 28                         eissn                 electronic ISSN
## 30                  elocation_id             Electronic Location
## 2                     everything         All text in the article
## 62              everything_ngram                            <NA>
## 63          everything_noprocess                            <NA>
## 64                everything_rev                            <NA>
## 65          figure_table_caption                            <NA>
## 41          financial_disclosure  Financial Disclosure Statement
## 1                             id DOI (Digital Object Identifier)
## 12                  introduction            Introduction section
## 24                         issue                           Issue
## 22                       journal               Full Journal Name
## 32             journal_id_nlm_ta               Journal ID at NLM
## 31                journal_id_pmc               Journal ID at PMC
## 33          journal_id_publisher       Publisher of this Journal
## 13         materials_and_methods   Materials and Methods section
## 35                     pagecount           Total number of pages
## 29                         pissn                      print ISSN
## 19              publication_date                Publication Date
## 34                     publisher       Publisher of this Article
## 20                 received_date                   Received Date
## 17                     reference               Reference section
## 14        results_and_discussion  Results and discussion section
## 26                       subject                Subject Category
## 66                 subject_facet                            <NA>
## 67             subject_hierarchy                            <NA>
## 27               subject_level_1                Subject Category
## 68                      subject2                            <NA>
## 69                subject2_facet                            <NA>
## 70            subject2_hierarchy                            <NA>
## 71              subject2_level_1                            <NA>
## 16        supporting_information  Supporting Information section
## 44                     timestamp              Time of last index
## 3                          title                   Article Title
## 4                  title_display                   Article Title
## 72                   title_ngram                            <NA>
## 23                        volume                          Volume
##                                                                           note
## 10                                                                     no note
## 49                                                                        <NA>
## 11                            For display purposes only. Primary abstract only
## 21                                                 Requires start and end date
## 38                                                    Can have multiple values
## 50                                                                        <NA>
## 5                                                                      no note
## 25                                                                     no note
## 51                                                                        <NA>
## 6                                                     Can have multiple values
## 52                                                                        <NA>
## 9                        For display purposes only. Collaborative authors only
## 7                                                    For display purposes only
## 53                                                                        <NA>
## 39                                                                     no note
## 8  For display purposes only. All the authors except for collaborative authors
## 18                                              Without Abstract or References
## 54                                                                        <NA>
## 55                                                                        <NA>
## 40                                                                     no note
## 15                                                                     no note
## 45                                                       copyright information
## 42                                                                     no note
## 43                                                                     no note
## 48         PLoS-specific indexes for articles that appear in multiple journals
## 47         PLoS-specific indexes for articles that appear in multiple journals
## 46         PLoS-specific indexes for articles that appear in multiple journals
## 56                                                                        <NA>
## 57                                                                        <NA>
## 58                                                                        <NA>
## 59                                                                        <NA>
## 36                                                    Can have multiple values
## 60                                                                        <NA>
## 37                                                  For display purposes only.
## 61                                                                        <NA>
## 28                                                                     no note
## 30                                                     Used by Pub Med Central
## 2                                                    Includes Meta information
## 62                                                                        <NA>
## 63                                                                        <NA>
## 64                                                                        <NA>
## 65                                                                        <NA>
## 41                                                                     no note
## 1                                               Extended for partial documents
## 12                                                                     no note
## 24                                                                     no note
## 22                                                                     no note
## 32                                    Used by the National Library of Medicine
## 31                                                     Used by Pub Med Central
## 33                                                            Short identifier
## 13                                                                     no note
## 35                                            Not all articles have page count
## 29                                                                     no note
## 19                                                 Requires start and end date
## 34                                                                   Full name
## 20                                                 Requires start and end date
## 17                                                    Can have multiple values
## 14                                                                     no note
## 26                                                    Can have multiple values
## 66                                                                        <NA>
## 67                                                                        <NA>
## 27             Can have multiple values. Contains only the top level subjects.
## 68                                                                        <NA>
## 69                                                                        <NA>
## 70                                                                        <NA>
## 71                                                                        <NA>
## 16                                                                     no note
## 44                                                                     no note
## 3                                                                      no note
## 4                                                    For display purposes only
## 72                                                                        <NA>
## 23                                                                     no note

# All the journals.
journalnamekey()

## [1] "PLoSONE"            "PLoSGenetics"       "PLoSPathogens"     
## [4] "PLoSNTD"            "PLoSCompBiol"       "PLoSBiology"       
## [7] "PLoSMedicine"       "PLoSClinicalTrials"

# Search for an author. Return the research paper ID and title.
searchplos(q = "author:sulzer", fl = "id, title")

## $meta
## # A tibble: 1 x 2
##   numFound start
##      <int> <int>
## 1       30     0
## 
## $data
## # A tibble: 10 x 2
##    id                             title                                    
##    <chr>                          <chr>                                    
##  1 10.1371/journal.pcbi.1005681   Self-regulation strategy, feedback timin…
##  2 10.1371/journal.pcbi.1005681/… <NA>                                     
##  3 10.1371/journal.pcbi.1005681/… <NA>                                     
##  4 10.1371/journal.pcbi.1005681/… <NA>                                     
##  5 10.1371/journal.pcbi.1005681/… <NA>                                     
##  6 10.1371/journal.pcbi.1005681/… <NA>                                     
##  7 10.1371/journal.pcbi.1005681/… <NA>                                     
##  8 10.1371/journal.pcbi.1005681/… <NA>                                     
##  9 10.1371/journal.pone.0053040   Macroautophagy Abnormality in Essential …
## 10 10.1371/journal.pone.0053040/… <NA>

# Query on a keyword. Return ID, date, title, and abstract.
p <- searchplos(q = "synuclein",
                fl = c("id", "publication_date", "title", "abstract"),
                limit = 20)

# The number of hits.
p$meta

## # A tibble: 1 x 2
##   numFound start
##      <int> <int>
## 1     1967     0

# The data.
p$data

## # A tibble: 20 x 4
##    id          publication_date  abstract               title              
##    <chr>       <chr>             <chr>                  <chr>              
##  1 10.1371/jo… 2013-04-22T00:00… "\nAbnormal α-synucle… Molecular Ageing o…
##  2 10.1371/jo… 2014-02-25T00:00… "\nα-Synuclein is the… Differential Expre…
##  3 10.1371/jo… 2011-01-31T00:00… "\n        Genetic an… Resistance to MPTP…
##  4 10.1371/jo… 2012-12-31T00:00… "\n        α-Synuclei… p62/SQSTM1-Depende…
##  5 10.1371/jo… 2013-04-25T00:00… "α-synuclein dysregul… Alpha-Synuclein In…
##  6 10.1371/jo… 2012-08-08T00:00… "\n        Phospholip… γ-Synuclein Intera…
##  7 10.1371/jo… 2011-07-14T00:00… "\n        Genetic, b… Assessment of α-Sy…
##  8 10.1371/jo… 2012-12-17T00:00… "\n        α-synuclei… α-Synuclein and An…
##  9 10.1371/jo… 2013-02-20T00:00… "\n        Cigarette … Human α4β2 Nicotin…
## 10 10.1371/jo… 2010-05-05T00:00… "Background: Melanoma… Parkinson's Diseas…
## 11 10.1371/jo… 2013-01-22T00:00… "\n        Amyloid fi… Temperature-Depend…
## 12 10.1371/jo… 2011-12-07T00:00… "\n        Alpha-synu… Redistribution of …
## 13 10.1371/jo… 2014-07-07T00:00… "\nSynucleinopathies,… Novel AAV-Based Ra…
## 14 10.1371/jo… 2015-04-06T00:00… "\nThere is unequivoc… Alpha-Synuclein Le…
## 15 10.1371/jo… 2013-05-07T00:00… "\nWhile most forms o… Impairment of Mito…
## 16 10.1371/jo… 2011-10-31T00:00… "\n        Recent res… Antibodies against…
## 17 10.1371/jo… 2010-08-11T00:00… "\nThe protein α-synu… α-Synuclein Suppre…
## 18 10.1371/jo… 2009-08-14T00:00… "\nIn synucleinopathi… Parkin Deficiency …
## 19 10.1371/jo… 2017-02-10T00:00… "\nα-Synuclein misfol… α-Synuclein increa…
## 20 10.1371/jo… 2012-04-27T00:00… "\n        α-Synuclei… Role of Alpha-Synu…

Explore one patent at a time

• appId: US6226553B1
• title: US5689004ADiamines containing pendent phenylethynyl groups
• search: Find papers whose body contains the term “phenylethynyl”.

p <- searchplos(q = "body:phenylethynyl",
                fl = c("id", "publication_date", "title", "abstract"))

# The number of hits.
p$meta

## # A tibble: 1 x 2
##   numFound start
##      <int> <int>
## 1       40     0

# The data.
p$data

## # A tibble: 10 x 4
##    id          publication_date  abstract               title              
##    <chr>       <chr>             <chr>                  <chr>              
##  1 10.1371/jo… 2014-05-23T00:00… "\nWe studied pattern… Structural Probing…
##  2 10.1371/jo… 2014-12-30T00:00… "\nSquare wave voltam… Square Wave Voltam…
##  3 10.1371/jo… 2016-10-17T00:00… "\nPenicillin binding… Oxazin-5-Ones as a…
##  4 10.1371/jo… 2011-06-17T00:00… "\n        Highly sel… Design, Synthesis …
##  5 10.1371/jo… 2007-08-22T00:00… Optimization of a ser… Computer-Aided Lea…
##  6 10.1371/jo… 2012-10-23T00:00… "\n        Drug toxic… In Situ Mass Spect…
##  7 10.1371/jo… 2014-07-25T00:00… "\nAntagonists of met… The mGluR5 Antagon…
##  8 10.1371/jo… 2008-05-14T00:00… Hippocampal synaptic … MGluR5 Mediates th…
##  9 10.1371/jo… 2017-11-27T00:00… "\nPrion infections c… Inhibition of grou…
## 10 10.1371/jo… 2019-08-21T00:00… "\nInhibitory glycine… mGluR5/ERK signali…

• appId: US6261844B1
• title: US5730806AGas-liquid supersonic cleaning and cleaning verification spray system
• search: - search: Find papers whose body contains the term “liquid supersonic cleaning.”

p <- searchplos(q = "body:liquid supersonic cleaning",
                fl = c("id", "publication_date", "title", "abstract"))

# The number of hits.
p$meta

## # A tibble: 1 x 2
##   numFound start
##      <int> <int>
## 1       13     0

# The data.
p$data

## # A tibble: 10 x 4
##    id          publication_date  abstract               title              
##    <chr>       <chr>             <chr>                  <chr>              
##  1 10.1371/jo… 2013-07-16T00:00… "\nWe report on the s… Nanoscale Roughnes…
##  2 10.1371/jo… 2018-06-01T00:00… "\nSugarcane bagasse … A comparative stud…
##  3 10.1371/jo… 2013-11-11T00:00… "\nThe development of… Comparison of Nume…
##  4 10.1371/jo… 2017-04-11T00:00… "\nThe considerable m… Hydrophobic pinnin…
##  5 10.1371/jo… 2014-01-21T00:00… "\nExenatide is an FD… Oral Delivery of E…
##  6 10.1371/jo… 2019-10-24T00:00… "\nArraying individua… Extracellular vesi…
##  7 10.1371/jo… 2016-10-27T00:00… "\nThe Cu-Li-Sn phase… The Cu-Li-Sn Phase…
##  8 10.1371/jo… 2017-06-08T00:00… "\nThe recent episode… Application of aco…
##  9 10.1371/jo… 2012-12-05T00:00… "Objective: Chronic r… Macrophages Facili…
## 10 10.1371/jo… 2012-02-20T00:00… "\n        There has … Quantitative Model…

Title terms

Search for papers whose bodies contain the top words from NASA patent titles.

top_title_words <- title_words %>% top_n(10)

## Selecting by n

top_title_words <- top_title_words$word

for (word in top_title_words) {
  q <-  paste0("body:", word)
  p <- searchplos(q = q,
                  fl = c("id", "publication_date", "title", "abstract"))
  
  print(paste0("Search term: ", word))
  print(paste0("Count: ", p$meta$numFound))
  print(paste0("Sample of documents found..."))
  print(head(p$data), n = 5)
}

## [1] "Search term: carbon"
## [1] "Count: 29619"
## [1] "Sample of documents found..."
## # A tibble: 6 x 4
##   id          publication_date  abstract               title               
##   <chr>       <chr>             <chr>                  <chr>               
## 1 10.1371/jo… 2013-08-26T00:00… "\nChina has been exp… Organic Carbon Stor…
## 2 10.1371/jo… 2013-01-10T00:00… "\n        Phenotypic… Coevolution Trumps …
## 3 10.1371/jo… 2012-09-14T00:00… "\n        Monitoring… Towards Regional, E…
## 4 10.1371/jo… 2015-03-16T00:00… "\nSoil type and fert… Dynamics of Maize C…
## 5 10.1371/jo… 2016-08-05T00:00… "\nThe alpine grassla… Ecosystem Carbon St…
## # … with 1 more row
## [1] "Search term: sensor"
## [1] "Count: 16289"
## [1] "Sample of documents found..."
## # A tibble: 6 x 4
##   id          publication_date  abstract               title               
##   <chr>       <chr>             <chr>                  <chr>               
## 1 10.1371/jo… 2018-08-29T00:00… "\nBuilding predictiv… Infinitely large, r…
## 2 10.1371/jo… 2015-10-23T00:00… "\nMagnetic biosensor… Configurational Sta…
## 3 10.1371/jo… 2018-10-09T00:00… "Background: In diabe… Effect of sensor lo…
## 4 10.1371/jo… 2015-05-07T00:00… "\nDetecting spreadin… Detecting the Influ…
## 5 10.1371/jo… 2014-03-04T00:00… "\nWe address the pro… Feature Selection f…
## # … with 1 more row
## [1] "Search term: based"
## [1] "Count: 229327"
## [1] "Sample of documents found..."
## # A tibble: 6 x 4
##   id          publication_date  abstract               title               
##   <chr>       <chr>             <chr>                  <chr>               
## 1 10.1371/jo… 2010-05-10T00:00… "Background: During t… Transcription-Assoc…
## 2 10.1371/jo… 2016-11-02T00:00… "\nLander-Waterman’s … Breaking Lander-Wat…
## 3 10.1371/jo… 2015-12-07T00:00… "\nThe introduction o… Introducing Compute…
## 4 10.1371/jo… 2017-10-27T00:00… "\nThe challenge of d… Hybrid self-optimiz…
## 5 10.1371/jo… 2018-01-31T00:00… "Background: The last… Historical trends i…
## # … with 1 more row
## [1] "Search term: composite"
## [1] "Count: 60646"
## [1] "Sample of documents found..."
## # A tibble: 6 x 4
##   id          publication_date  abstract               title               
##   <chr>       <chr>             <chr>                  <chr>               
## 1 10.1371/jo… 2017-01-20T00:00… "\nMussel-inspired ap… Thermal Conductivit…
## 2 10.1371/jo… 2018-04-13T00:00… "Objective: To study … Preparation and cha…
## 3 10.1371/jo… 2012-02-08T00:00… "\n        Most biolo… Composite Structura…
## 4 10.1371/jo… 2015-12-29T00:00… "\nAny release of ant… Composite Sampling …
## 5 10.1371/jo… 2018-09-24T00:00… "\nProteins with low-… Proteome-scale rela…
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## 2 10.1371/jo… 2018-03-05T00:00… "\nCell size is thoug… A computational mod…
## 3 10.1371/jo… 2016-07-25T00:00… "\nThe rubber hand il… ‘Robot’ Hand Illusi…
## 4 10.1371/jo… 2012-12-20T00:00… "\n        Natural an… A Genome-Wide Inves…
## 5 10.1371/jo… 2011-02-02T00:00… "\n        The Arabid… Sense and Antisense…
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## [1] "Sample of documents found..."
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## 1 10.1371/jo… 2016-10-26T00:00… "Purpose: To investig… The Effect of Optic…
## 2 10.1371/jo… 2012-09-04T00:00… "Background: Research… A Novel Animal Mode…
## 3 10.1371/jo… 2015-10-01T00:00… "Purpose: To assess t… Glaucomatous-Type O…
## 4 10.1371/jo… 2007-02-07T00:00… "\n            The re… A Functional Archit…
## 5 10.1371/jo… 2009-10-13T00:00… "\nIn this Research A… Dynamic Coupling of…
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## 1 10.1371/jo… 2018-10-23T00:00… "\nOrganizational pro… Development and val…
## 2 10.1371/jo… 2010-12-03T00:00… "\n        We present… Feller Processes: T…
## 3 10.1371/jo… 2017-04-27T00:00… "\nMetabolic disorder… Tracking disease pr…
## 4 10.1371/jo… 2019-02-20T00:00… "\nTropidolaemus wagl… Description of cran…
## 5 10.1371/jo… 2009-04-23T00:00… "Background: The trad… Biological Process …
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## 1 10.1371/jo… 2017-03-13T00:00… "\nThermal properties… Measurement of ther…
## 2 10.1371/jo… 2011-06-17T00:00… "\n        Most organ… Isopods Failed to A…
## 3 10.1371/jo… 2014-12-22T00:00… "\nThermal energy tra… Thermophysical Prop…
## 4 10.1371/jo… 2016-02-03T00:00… "\nClimate change is … Ontogenetic Variati…
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## 1 10.1371/jo… 2014-12-26T00:00… "Objective: To evalua… Ranibizumab Monothe…
## 2 10.1371/jo… 2018-09-06T00:00… "\nPicosecond lasers … Effects of picoseco…
## 3 10.1371/jo… 2018-11-29T00:00… "\nThe nucleus accumb… Optogenetic self-st…
## 4 10.1371/jo… 2015-07-10T00:00… "\nThe mouse model of… Optimization of an …
## 5 10.1371/jo… 2013-12-11T00:00… "\nSafe and effective… Near-Infrared Laser…
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## 1 10.1371/jo… 2015-05-29T00:00… "\nThe objective of t… Optimal Design of R…
## 2 10.1371/jo… 2015-05-08T00:00… "\nIn this study, we … Do Parents Meet Ado…
## 3 10.1371/jo… 2013-02-28T00:00… "Objectives: Mortalit… Monitoring of Antir…
## 4 10.1371/jo… 2015-03-20T00:00… "Background: The cost… The Cost-Effectiven…
## 5 10.1371/jo… 2017-08-23T00:00… "\nPatients with Park… Verbal monitoring i…
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## 4 10.1371/jo… 2012-05-24T00:00… "\n        We present… Carbon Nanotube Sol…
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Resources

Collaboration

• Work Breakdown Structure.

References

• The WIPO Manual on Open Source Patent Analytics. An end-to-end guide for information retrieval for patents and research papers.
• How AI Can Tame the Scientific Literature. Survey of Iris.ai, Semantic Scholar from the Allen Institute for Artificial Intelligence, Microsoft Academic, etc. Iris.ai sources CORE.

Agencies and services

• WIPO. The World Intellectual Property Organization.
• Public Library of Science. PLOS. 200K+ open access peer-reviewed journal articles.
• CORE. Claims to be the largest aggregator of open access research papers, with an inventory of 135M+. Provides APIs for text mining.
• rOpenSci. Non-profit that addresses reproducibility of scientific data retrieval. A raft of R packages for working with scientific data sources and supporting research. Leadership includes Jenny Bryan and advisors include Hadley Wickham. Blog, tutorials, and videos.
• Allen Institute for Artificial Intelligence. Semantic Scholar, etc.
• One World Analytics. Analyzes social and environmental issues using text mining, statistics, geographic mapping and network visualisation.

NASA

• NASA’s Open Data Portal.
• NASA Patents.

US patent data

• USPTO Open Data Portal. United States Patent and Trademark Office data products. Bulk downloads, APIs, analytics, etc.
• PatentsView. Secondary source, with dataset downloads and APIs.

R packages

• rplos Vignette. Interface to the Solr based search API for PLOS journals. Functions search for articles, retrieve articles, make plots, do faceted searches, highlight searches, and present results of highlighted searches in a browser.
• fulltext Vignette. Facilitates text mining with an emphasis on open access journals. The vignette provides examples.

APIs

• PLOS API. Documentation, articles, frequently asked questions.

Examples

• PLos Article Classification. A Python project on GitHub using machine learning to classify scientific articles by subject.
• Open NASA Data from API to Data Analysis. YouTube video by Noemi Derzsy, NASA Datanaut, from the 2017 NYC SpaceApps Conference. Easy introduction to data obtainable from NASA.
• Mosaic Knowledge Graphs. Allen Institute. Graph network analysis of text.